Abstract — A vital problem in evaluating quality of a picture in image compression system is the difficulty in describing the type and amount of degradation in reconstructed image. Because of the inherent drawbacks associated with various subjective measures of assessing the picture quality great emphasis is laid in developing substitute objective measures. Owing to the parallel architecture of Neural Networks and their generalization ability to provide output data for previously unseen input data, In this work we proposed and implemented two approaches of image compression, The first one a hybrid approach involved the usage of neural network, scalar quantizer and Huffman encoder for compressing the input image thrice in a sequence successively where the neural network is trained with LM- Algorithm and RP-Algorithm separately for analysing the metrics. The second approach involves a neural network model alone for 2-d data encoding and decoding where the network is again trained with error Backpropagation and Resilient Backpropagation algorithms separately. We have considered few bench mark images Lena, Cameraman, Peppers and Trees etc for the entire analysis, few objective quality measurable criteria – the PSNR, MSE and CR are tabulated for analysis in both the approaches. Keywords—Entropy encoding, Levenberg-Marquardt, MSE, Neural Networks, Quantization. I
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